Title :
Adaptive Hopfield neural networks for economic load dispatch
Author :
Lee, Kwang Y. ; Sode-Yome, Arthit ; Park, June Ho
Author_Institution :
Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA
fDate :
5/1/1998 12:00:00 AM
Abstract :
A large number of iterations and oscillations are those of the major concern in solving the economic load dispatch problem using the Hopfield neural network. This paper develops two different methods, the slope adjustment and bias adjustment methods, in order to speed up the convergence of the Hopfield neural network system. Algorithms of economic load dispatch for piecewise quadratic cost functions using the Hopfield neural network have been developed for the two approaches. The results are compared with those of a numerical approach and the traditional Hopfield neural network approach. To guarantee and for faster convergence, adaptive learning rates are also developed by using energy functions and applied to the slope and bias adjustment methods. The results of the traditional, fixed learning rate and adaptive learning rate methods are compared in economic load dispatch problems
Keywords :
Hopfield neural nets; control system analysis; convergence of numerical methods; economics; load dispatching; neurocontrollers; power system control; adaptive Hopfield neural networks; adaptive learning rates; bias adjustment method; economic load dispatch; energy functions; numerical convergence; piecewise quadratic cost functions; power systems; slope adjustment method; Adaptive systems; Convergence; Cost function; Environmental economics; Fuel economy; Hopfield neural networks; Power generation; Power generation economics; Power system economics; Propagation losses;
Journal_Title :
Power Systems, IEEE Transactions on